As Business Intelligence tools become more and more prevalent, the focus for many organisations now is on how to get the most out of their investment. In this article we explore how a robust data management system can help you to gain greater insights from your BI tool.
The amount of data available to organisations is growing exponentially. Companies have so many customer touch points that they are able to gather an unprecedented amount of information about their audience. Finding the right information, understanding its quality and producing good data in a timely and cost-effective manner, therefore, are all critical issues. The rise in the use of BI tools when presented with all this information is therefore predictable. They are designed to retrieve, transform and report data ready for analysis, which can save organisations thousands of hours in manual processes as well as providing far more accurate insight.
But BI tools are only as good as the information you put into them. And as organisations collect greater amounts of data from different departments, it needs to be prepared in such a way that the chosen BI tool can readily report on it. This is where having a data management system becomes invaluable.
What is data management?
Data management is the process of connecting to various data sources, collecting and unifying business data, integrating it into a secure, governed hub and preparing it for expert analysis within the chosen BI tool.
It’s worth noting, data management and data governance are two different things, despite often being used interchangeably. Data management is comprised of the policies, procedures, practices and tools that are used to enhance data assets. Data governance, on the other hand, is the enforcement of said policies, procedures, practices and tools. As part of your data management, therefore, you will need to incorporate data governance.
Data governance and security may be at risk when manually preparing your data for analysis with a BI tool. This could be due to human error or a lack of process or audit trail. Additional workload and consultancy hours may also be required to handle everything from reconfiguring mismatched data definitions to controlling access. A good data management tool will remove these concerns and overheads as it automates, controls and logs all aspects of data governance and security.
Successful data management initiatives require a holistic approach that addresses the people within the organisation, the processes adopted and the technology used.
When it comes to improving data quality, a company culture that recognises data as an important factor for generating insights is essential. In the context of data quality and data management, assigning responsibilities for this data plays a crucial role.
Role concepts help with the definition and assignment of tasks to certain employees. By assigning these roles, the company is able to ensure that responsibility for accurate data and its care is clear.
Typical roles for ensuring data quality and data management are, Data Owners, Data Stewards, Data Managers and Data Users, with each role involving clear tasks that are geared towards company-specific goals.
Briefly, each role can be defined as:
- Data Owners – Ensure data quality, assign access rights and authorises data stewards to manage data
- Data Stewards – Define rules, plan requirements and coordinate data delivery
- Data Managers – Implement the requirements of the data owner, manage the technological infrastructure and secure access protection
- Data Users – Have access to the data
Having a set process that the organisation can buy into is a great way of improving and ensuring high data quality. The process will only work, however, if it can emphasise that data quality is not a one-time project but an ongoing undertaking.
This process should be made up of the following phases which can be assigned to the aforementioned roles:
- Define data quality goals according to business needs
- Analyse the data to ensure it is valid and accurate
- Cleanse the data according to the individual business needs
- Monitor and check to ensure ongoing protection of data quality
Most of the technologies on the market today are aligned with this data quality process and provide in-depth functionality to assist the various user roles and their part in the process.
Having a good data management tool can help to significantly reduce the IT overheads associated with accessing and preparing data for analysis with BI tools. An automated and intuitive interface will replace inefficient and inaccurate manual processes, as well as the need for time-consuming and expensive data warehouse projects. It should also deliver automated data governance and security.
What is the impact of good data management?
Not implementing a data management system can at best lead to inconsistencies in how metrics are defined across teams. At worst, incorrect data may end up being used for as the basis for business decisions. Inconsistencies in reporting as well as duplicated data and reports will result in time wasted reconciling information as well as an overall mistrust of the data. Even the best BI programs can fail quickly if the users don’t trust the information it provides. And once that trust in the information gained from a BI application dissolves, it is very hard to get back.
The benefit of setting up a data management program before implementing your BI tool is that, although data management can exist on its own, BI tools really need data management to be successful. If you establish your data management program first, you will set the stage for successful BI. This can result in improved data quality, consistent data standards and metric definitions and a standard BI architecture providing the foundation for your data users.
The rewards of a properly executed business intelligence program speak for themselves. Every business wants increased visibility into the factors that are affecting it so that they can make the best decisions possible. Similarly, most companies, before starting adopting BI tool, have some rather large degree of doubt about the accuracy and completeness of their data. Those BI practitioners among us know not to wait for “perfect data”, but rather to get started now on a path to near-perfect data – a path that begins with good data management.